Generating concept images of human poses using machine learning models

ABSTRACT

Methods, systems, and computer program products for generating concept images of human poses using machine learning models are provided herein. A computer-implemented method includes identifying one or more events from input data by applying a machine learning recognition model to the input data, wherein the identifying comprises (i) detecting multiple entities from the input data and (ii) determining one or more behavioral relationships among the multiple entities in the input data; generating, using a machine learning interpretability model and the identified events, one or more images illustrating one or more human poses related to the identified events; outputting the one or more generated images to at least one user; and updating the machine learning recognition model based at least in part on (i) the one or more generated images and (ii) input from the at least one user.

FIELD

The present application generally relates to information technology and,more particularly, to image creation technologies.

BACKGROUND

Conventional interpretability approaches are local in nature, typicallyhighlighting specific parts of an image which contribute towards aclassification task of the image. However, such approaches generallyfail to determine and/or learn relationships between objects in theimage, or to identify and/or determine a global concept of the image.

SUMMARY

In one embodiment of the present invention, techniques for generatingconcept images of human poses using machine learning models areprovided. An exemplary computer-implemented method can includeidentifying one or more events from input data by applying a machinelearning recognition model to the input data, wherein the identifyingcomprises (i) detecting multiple entities from the input data and (ii)determining one or more behavioral relationships among the multipleentities in the input data. Such a method also includes generating,using a machine learning interpretability model and the identifiedevents, one or more images illustrating one or more human poses relatedto the identified events, and outputting the one or more generatedimages to at least one user. Additionally, such a method includesupdating the machine learning recognition model based at least in parton (i) the one or more generated images and (ii) input from the at leastone user.

In another embodiment of the invention, an exemplarycomputer-implemented method includes training a machine learningrecognition model using data pertaining to human pose structures andimage texture information, and identifying one or more events from inputdata by applying the machine learning recognition model to the inputdata, wherein the identifying comprises (i) detecting multiple entitiesfrom the input data and (ii) determining one or more behavioralrelationships among the multiple entities in the input data. Such amethod also includes generating, using a machine learninginterpretability model and the identified events, one or more imagesillustrating one or more human poses related to the identified events,and outputting, via a dialog system, the one or more generated images toat least one human user. Further, such a method includes automaticallyproducing at least one machine-originated image representative of theone or more generated images based at least in part on feedback from theat least one human user, and updating the machine learning recognitionmodel based at least in part on the at least one machine-originatedimage.

Another embodiment of the invention or elements thereof can beimplemented in the form of a computer program product tangibly embodyingcomputer readable instructions which, when implemented, cause a computerto carry out a plurality of method steps, as described herein.Furthermore, another embodiment of the invention or elements thereof canbe implemented in the form of a system including a memory and at leastone processor that is coupled to the memory and configured to performnoted method steps. Yet further, another embodiment of the invention orelements thereof can be implemented in the form of means for carryingout the method steps described herein, or elements thereof; the meanscan include hardware module(s) or a combination of hardware and softwaremodules, wherein the software modules are stored in a tangiblecomputer-readable storage medium (or multiple such media).

These and other objects, features and advantages of the presentinvention will become apparent from the following detailed descriptionof illustrative embodiments thereof, which is to be read in connectionwith the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a diagram illustrating system architecture, according to anexemplary embodiment of the invention;

FIG. 2 is a diagram illustrating system architecture, according to anexemplary embodiment of the invention;

FIG. 3 is a diagram illustrating active learning and machine teaching,according to an exemplary embodiment of the invention;

FIG. 4 is a flow diagram illustrating techniques according to anembodiment of the invention;

FIG. 5 is a system diagram of an exemplary computer system on which atleast one embodiment of the invention can be implemented;

FIG. 6 depicts a cloud computing environment according to an embodimentof the present invention; and

FIG. 7 depicts abstraction model layers according to an embodiment ofthe present invention.

DETAILED DESCRIPTION

As described herein, an embodiment of the present invention includesgenerating concept images of human poses using machine learning models.At least one embodiment includes generating visual concept images ofhuman poses for higher-order concepts of human behavior. In contrast toconventional approaches for model interpretability, one or moreembodiments include using multimodal descriptions as input to generatethe concept images. Such an embodiment includes using a multimodalmachine learning algorithm, which learns a generalized concept of one ormore higher-order human behaviors and produces one or more visualconcept images. Such higher-order and/or generalized concepts aredefined as concepts which identify multiple entities and relationshipsbetween them.

At least one embodiment includes implementing a generation model whichis pre-trained to create stick figures which represent human poses,wherein the model is image-based, text-based, or multimodal. Such anembodiment includes providing an input to an interpretation pipelinewith a pre-trained stick figure generator, wherein an interpretabilitymodel generates various human poses. Additionally, in such anembodiment, a multimodal machine learning algorithm learns a generalizedconcept of a higher-order concept to generate a set of training images.As noted above and herein, an interpretation pipeline can include amodel or a series of modules which can be artificial intelligence- (AI-)based, non-AI-based, or mixed. In at least one embodiment, aninterpretation pipeline includes a diagnostic or evaluation moduleseparate from the actual/primary model (which may be image-based,text-based, or multimodal). In such an embodiment, a purpose of theinterpretation model is to confirm, by generating visually consistentscenes via stick figures, that the actual/primary model has understoodthe concept correctly.

One or more embodiments include using a vector representation of a humanpose (learnt by a machine learning model) and converting the vector toone or more human pose representations over one or more stick figures.In such an embodiment, a human user can subsequently interpret the oneor more stick figures and confirm the accuracy of the machine learningmodel output. Accordingly, at least one embodiment includes generatingimages of human poses by enabling the model to manipulate the pose ofthe stick figure(s) and create an image that is indicative (to a human)of a given concept.

As used herein, an interpretability model provides some provision toindicate the reason for a decision, such as the presence of correlatedattributes and/or a sequence of steps. By way merely of example, oneinstance of an interpretability model can include one or more decisiontrees. As also used herein, an explainability model justifies a decisionby providing human-interpretable evidence in a feature space. By way ofexample, with image/vision data or text data, such evidence can includefeature weight maps and/or heat maps (generated, for example, via guidedbackpropagation techniques, gradient-weighted class activation mappingtechniques, etc.). Additionally, as used herein, concepts in the visualdomain generally refer to colors, shapes, actions, etc., andhigher-order concepts are defined via learning and/or understandingacross data points.

FIG. 1 is a diagram illustrating system architecture, according to anembodiment of the invention. By way of illustration, FIG. 1 depicts anew knowledge (training) component 102, which applies model 104 (e.g., amachine learning model) to a new concept (i.e., input data pertaining toone or more human poses and/or behaviors). The output of model 104 is atleast one image, which is provided to a visual confirmation system 106.Upon a positive determination and/or approval by the visual confirmationsystem 106, the model output is deemed a concept image 108 and is outputby the visual confirmation system 106 to a human approver 110 foranalysis. Upon approval by the human approver 110, the model 104 isupdated based on the concept image 108. As noted above, the visualconfirmation system 106 is another term for an interpretation pipeline(detailed herein). Accordingly, the visual confirmation pipeline (whichcan be one or more modules with AI components) is a diagnostic orevaluation module separate from the actual/primary model 104 (which maybe image-based, text-based, or multimodal). As noted herein, a purposeof the visual confirmation system 106 is to confirm, by generatingvisually consistent scenes via stick figures, that the model 104 hasunderstood a concept correctly.

In an example embodiment such as depicted in FIG. 1, the model 104performs concept detection using pre-trained entity generation tools.The concept detected by the model 104 can be expressed in one or moresuitable forms, and output as an image or series of images (e.g.,concept image 108) that are subsequently presented to an end-user and/orhuman annotator (e.g., human approver 110) for confirmation and/orvalidation.

FIG. 2 is a diagram illustrating system architecture, according to anexemplary embodiment of the invention. By way of illustration, FIG. 2depicts a model 203 (e.g., a machine learning model), which is appliedto input data and outputs an identified and/or determined primary task.Additionally, FIG. 2 depicts a new knowledge (training) component 202,which updates model 203, thereby creating a new model 204, using a newconcept (i.e., input data pertaining to one or more human poses and/orbehaviors). The output of model 204 is at least one image, which isprovided to visual confirmation system 206. Upon a positivedetermination and/or approval by the visual confirmation system 206, themodel output is deemed a concept image 208 and is output by the visualconfirmation system 206 to a human approver 210 for analysis. Uponapproval by the human approver 210, the model 204 can be further updatedbased on the concept image 208.

Accordingly, at least one embodiment includes providing visualinterpretability of higher-order (human pose and/or behavior) concepts.Such an embodiment includes utilizing a paradigm of image retrievaland/or generation for visual imaging of the concepts. In one or moreembodiments, pre-trained models with pose structure and texture ofimages (such as, for example, stick figures) are used for imagegeneration. Higher-order concepts often require many words to describespatial aspects via text; however, one or more embodiments includeencompassing spatial configurations in one or more images.

As detailed herein, at least one embodiment includes implementing one ormore machine learning models to learn higher-order concepts from inputdata. As also noted herein, higher-order concepts include those thatrequire identifying multiple entities and relationships between them(e.g., sports including multiple players, a playing surface, multipleitems of playing equipment, etc.).

In one or more embodiments, a (machine learning) generation model ispre-trained to create stick figures representative of human poses. Insuch an embodiment, the human pose-to-stick figure model can include afully generative model and/or image retrieval from a corpus. As anexample, in such an embodiment, an event recognition model can betrained to learn and/or understand various events pertaining to humanposes and/or human behaviors. Such a model can be image-based, textbased, or multimodal. Also, in such an embodiment, the model is passed,as input, to a proposed interpretation pipeline with a pre-trained stickfigure generator. The interpretability model then generates varioushuman poses (illustrated via stick figures), and the visualized stickfigures of concepts are provided to one or more end-users forconfirmation and/or validation that the model is accurate and/or notbiased.

FIG. 3 is a diagram illustrating active learning and machine teaching,according to an exemplary embodiment of the invention. By way ofillustration, FIG. 3 depicts an embodiment that includes active learningand machine teaching. In such an embodiment, a visual confirmation model324 generates a stick figure collage to confirm the understanding ofspecific human behavior(s). As also depicted in FIG. 3, such anembodiment includes utilizing a pre-trained visual recognition system322 that is built on various modules of predicted sub-tasks. Such anembodiment also includes utilizing a machine teaching scenario 326,which includes using a dialog system 320 to interact with a humanteacher and/or approver to understand and/or confirm higher-order visualconcepts. In such an embodiment, the dialog system 320 is used tounderstand higher-order-encompassing concepts from known lower-orderconcepts related to human behavior(s). Additionally, in at least oneembodiment, the visual confirmation model 324 is constrained to generatestick figures of humans (and human poses and/or behaviors). Based on thedialog with the human teacher (e.g., the machine teaching scenario 326),the visual confirmation model 324 learns at least one concept andconfirms this knowledge with at least one machine-generated image.

Also, one or more embodiments include image generation using white boxmultimodal settings. A white box model, as used herein, refers to an AImodel which allows access to its internal workings and/or components.For example, one such white box model can include a neural network thatprovides access to intermediate layers and/or representations. A goal ofsuch an embodiment includes creating images that confirm a higher-orderunderstanding of visual concepts related to human poses and/orbehavior(s). In at least one such embodiment, images of specific (new)pose concepts are input to a multimodal learning model, which outputs aprimary task and one or more generalized images that confirm theconcept.

Further, in such an embodiment, a pre-trained human pose estimationcomponent, in connection with the multimodal learning model, computesjoin positions and gaze determine poses. The multimodal learning modelthen generalizes over specific instances of pose estimation that arelabeled as a single higher-order concept (such as, for example, “groupexercise classes”). The multimodal machine learning algorithm (used aspart of the model) attempts to learn a generalized concept of thishigher-order concept. Additionally, a visual confirmation model uses theintermediary representation of the base model and generates conceptimages to generalize the concept images (such as, for example, depictedin FIG. 2). Further, in one or more embodiments, the visual confirmationis separate from the primary task.

Also, one or more embodiments include incorporating situationalunderstanding in generating concept images of human poses using machinelearning models. Such an embodiment includes extending the visualconfirmation system in the machine teaching situation to propagate theunderstanding in one situation into another situation. In such anembodiment, the visual confirmation system not only confirms the pose orhigher-order concept, but also generalizes the pose or higher-orderconcept to one or more other situations. In an example embodiment, thevisual confirmation system, upon receiving human approval to update themodel (such as depicted in FIG. 3, for instance) updates a graphicalmodel and/or a knowledge graph with this new confirmed understanding ofthe scene. The graphical model and/or knowledge graph can then be usedto infer anew relationship between other objects in the scene. Forexample, the machine teaching component can learn that cold and windyweather causes humans to shiver, and the update to the graphical modelcan reduce the chance of determining an action of flying a kiteaccordingly.

FIG. 4 is a flow diagram illustrating techniques according to anembodiment of the present invention. Step 402 includes identifying oneor more events from input data by applying a machine learningrecognition model to the input data, wherein identifying comprises (i)detecting multiple entities from the input data and (ii) determining oneor more behavioral relationships among the multiple entities in theinput data. The input data can include image data, text data, and/ormultimodal data. At least one embodiment additionally includes trainingthe machine learning recognition model using data pertaining to humanpose structures and image texture information.

Step 404 includes generating, using a machine learning interpretabilitymodel and the identified events, one or more images illustrating one ormore human poses related to the identified events. In at least oneembodiment, the one or more images include one or more stick figureimages.

Also, in one or more embodiments, identifying one or more events caninclude generating, via the machine learning recognition model, a vectorrepresentation of one or more human poses related to the identifiedevents. In such an embodiment, generating the one or more imagesincludes converting the vector representation to one or more human poserepresentations over one or more stick figure images.

Step 406 includes outputting the one or more generated images to atleast one user. Step 408 includes updating the machine learningrecognition model based at least in part on (i) the one or moregenerated images and (ii) input from the at least one user.

Also, an additional embodiment of the invention includes training amachine learning recognition model using data pertaining to human posestructures and image texture information, and identifying one or moreevents from input data by applying the machine learning recognitionmodel to the input data, wherein the identifying comprises (i) detectingmultiple entities from the input data and (ii) determining one or morebehavioral relationships among the multiple entities in the input data.Such an embodiment also includes generating, using a machine learninginterpretability model and the identified events, one or more imagesillustrating one or more human poses related to the identified events,and outputting, via a dialog system, the one or more generated images toat least one human user. Further, such an embodiment includesautomatically producing at least one machine-originated imagerepresentative of the one or more generated images based at least inpart on feedback from the at least one human user, and updating themachine learning recognition model based at least in part on the atleast one machine-originated image.

The techniques depicted in FIG. 4 can also, as described herein, includeproviding a system, wherein the system includes distinct softwaremodules, each of the distinct software modules being embodied on atangible computer-readable recordable storage medium. All of the modules(or any subset thereof) can be on the same medium, or each can be on adifferent medium, for example. The modules can include any or all of thecomponents shown in the figures and/or described herein. In anembodiment of the invention, the modules can run, for example, on ahardware processor. The method steps can then be carried out using thedistinct software modules of the system, as described above, executingon a hardware processor. Further, a computer program product can includea tangible computer-readable recordable storage medium with code adaptedto be executed to carry out at least one method step described herein,including the provision of the system with the distinct softwaremodules.

Additionally, the techniques depicted in FIG. 4 can be implemented via acomputer program product that can include computer useable program codethat is stored in a computer readable storage medium in a dataprocessing system, and wherein the computer useable program code wasdownloaded over a network from a remote data processing system. Also, inan embodiment of the invention, the computer program product can includecomputer useable program code that is stored in a computer readablestorage medium in a server data processing system, and wherein thecomputer useable program code is downloaded over a network to a remotedata processing system for use in a computer readable storage mediumwith the remote system.

An embodiment of the invention or elements thereof can be implemented inthe form of an apparatus including a memory and at least one processorthat is coupled to the memory and configured to perform exemplary methodsteps.

Additionally, an embodiment of the present invention can make use ofsoftware running on a computer or workstation. With reference to FIG. 5,such an implementation might employ, for example, a processor 502, amemory 504, and an input/output interface formed, for example, by adisplay 506 and a keyboard 508. The term “processor” as used herein isintended to include any processing device, such as, for example, onethat includes a CPU (central processing unit) and/or other forms ofprocessing circuitry. Further, the term “processor” may refer to morethan one individual processor. The term “memory” is intended to includememory associated with a processor or CPU, such as, for example, RAM(random access memory), ROM (read only memory), a fixed memory device(for example, hard drive), a removable memory device (for example,diskette), a flash memory and the like. In addition, the phrase“input/output interface” as used herein, is intended to include, forexample, a mechanism for inputting data to the processing unit (forexample, mouse), and a mechanism for providing results associated withthe processing unit (for example, printer). The processor 502, memory504, and input/output interface such as display 506 and keyboard 508 canbe interconnected, for example, via bus 510 as part of a data processingunit 512. Suitable interconnections, for example via bus 510, can alsobe provided to a network interface 514, such as a network card, whichcan be provided to interface with a computer network, and to a mediainterface 516, such as a diskette or CD-ROM drive, which can be providedto interface with media 518.

Accordingly, computer software including instructions or code forperforming the methodologies of the invention, as described herein, maybe stored in associated memory devices (for example, ROM, fixed orremovable memory) and, when ready to be utilized, loaded in part or inwhole (for example, into RAM) and implemented by a CPU. Such softwarecould include, but is not limited to, firmware, resident software,microcode, and the like.

A data processing system suitable for storing and/or executing programcode will include at least one processor 502 coupled directly orindirectly to memory elements 504 through a system bus 510. The memoryelements can include local memory employed during actual implementationof the program code, bulk storage, and cache memories which providetemporary storage of at least some program code in order to reduce thenumber of times code must be retrieved from bulk storage duringimplementation.

Input/output or I/O devices (including, but not limited to, keyboards508, displays 506, pointing devices, and the like) can be coupled to thesystem either directly (such as via bus 510) or through intervening I/Ocontrollers (omitted for clarity).

Network adapters such as network interface 514 may also be coupled tothe system to enable the data processing system to become coupled toother data processing systems or remote printers or storage devicesthrough intervening private or public networks. Modems, cable modems andEthernet cards are just a few of the currently available types ofnetwork adapters.

As used herein, including the claims, a “server” includes a physicaldata processing system (for example, system 512 as shown in FIG. 5)running a server program. It will be understood that such a physicalserver may or may not include a display and keyboard.

The present invention may be a system, a method, and/or a computerprogram product at any possible technical detail level of integration.The computer program product may include a computer readable storagemedium (or media) having computer readable program instructions thereonfor causing a processor to carry out aspects of the present invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, configuration data for integrated circuitry, oreither source code or object code written in any combination of one ormore programming languages, including an object oriented programminglanguage such as Smalltalk, C++, or the like, and procedural programminglanguages, such as the “C” programming language or similar programminglanguages. The computer readable program instructions may executeentirely on the user's computer, partly on the user's computer, as astand-alone software package, partly on the user's computer and partlyon a remote computer or entirely on the remote computer or server. Inthe latter scenario, the remote computer may be connected to the user'scomputer through any type of network, including a local area network(LAN) or a wide area network (WAN), or the connection may be made to anexternal computer (for example, through the Internet using an InternetService Provider). In some embodiments, electronic circuitry including,for example, programmable logic circuitry, field-programmable gatearrays (FPGA), or programmable logic arrays (PLA) may execute thecomputer readable program instructions by utilizing state information ofthe computer readable program instructions to personalize the electroniccircuitry, in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a computer, or other programmable data processing apparatusto produce a machine, such that the instructions, which execute via theprocessor of the computer or other programmable data processingapparatus, create means for implementing the functions/acts specified inthe flowchart and/or block diagram block or blocks. These computerreadable program instructions may also be stored in a computer readablestorage medium that can direct a computer, a programmable dataprocessing apparatus, and/or other devices to function in a particularmanner, such that the computer readable storage medium havinginstructions stored therein comprises an article of manufactureincluding instructions which implement aspects of the function/actspecified in the flowchart and/or block diagram block or blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the blocks may occur out of theorder noted in the Figures. For example, two blocks shown in successionmay, in fact, be accomplished as one step, executed concurrently,substantially concurrently, in a partially or wholly temporallyoverlapping manner, or the blocks may sometimes be executed in thereverse order, depending upon the functionality involved. It will alsobe noted that each block of the block diagrams and/or flowchartillustration, and combinations of blocks in the block diagrams and/orflowchart illustration, can be implemented by special purposehardware-based systems that perform the specified functions or acts orcarry out combinations of special purpose hardware and computerinstructions.

It should be noted that any of the methods described herein can includean additional step of providing a system comprising distinct softwaremodules embodied on a computer readable storage medium; the modules caninclude, for example, any or all of the components detailed herein. Themethod steps can then be carried out using the distinct software modulesand/or sub-modules of the system, as described above, executing on ahardware processor 502. Further, a computer program product can includea computer-readable storage medium with code adapted to be implementedto carry out at least one method step described herein, including theprovision of the system with the distinct software modules.

In any case, it should be understood that the components illustratedherein may be implemented in various forms of hardware, software, orcombinations thereof, for example, application specific integratedcircuit(s) (ASICS), functional circuitry, an appropriately programmeddigital computer with associated memory, and the like. Given theteachings of the invention provided herein, one of ordinary skill in therelated art will be able to contemplate other implementations of thecomponents of the invention.

Additionally, it is understood in advance that implementation of theteachings recited herein are not limited to a particular computingenvironment. Rather, embodiments of the present invention are capable ofbeing implemented in conjunction with any type of computing environmentnow known or later developed.

For example, cloud computing is a model of service delivery for enablingconvenient, on-demand network access to a shared pool of configurablecomputing resources (for example, networks, network bandwidth, servers,processing, memory, storage, applications, virtual machines, andservices) that can be rapidly provisioned and released with minimalmanagement effort or interaction with a provider of the service. Thiscloud model may include at least five characteristics, at least threeservice models, and at least four deployment models.

Characteristics are as follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher levelof abstraction (for example, country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (for example, storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported providing transparency for both theprovider and consumer of the utilized service.

Service Models are as follows:

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client devices through athin client interface such as a web browser (for example, web-basede-mail). The consumer does not manage or control the underlying cloudinfrastructure including network, servers, operating systems, storage,or even individual application capabilities, with the possible exceptionof limited user-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (for example, host firewalls).

Deployment Models are as follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(for example, mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (for example, cloud burstingfor load-balancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure comprising anetwork of interconnected nodes.

Referring now to FIG. 6, illustrative cloud computing environment 50 isdepicted. As shown, cloud computing environment 50 includes one or morecloud computing nodes 10 with which local computing devices used bycloud consumers, such as, for example, personal digital assistant (PDA)or cellular telephone 54A, desktop computer 54B, laptop computer 54C,and/or automobile computer system 54N may communicate. Nodes 10 maycommunicate with one another. They may be grouped (not shown) physicallyor virtually, in one or more networks, such as Private, Community,Public, or Hybrid clouds as described hereinabove, or a combinationthereof. This allows cloud computing environment 50 to offerinfrastructure, platforms and/or software as services for which a cloudconsumer does not need to maintain resources on a local computingdevice. It is understood that the types of computing devices 54A-N shownin FIG. 6 are intended to be illustrative only and that computing nodes10 and cloud computing environment 50 can communicate with any type ofcomputerized device over any type of network and/or network addressableconnection (e.g., using a web browser).

Referring now to FIG. 7, a set of functional abstraction layers providedby cloud computing environment 50 (FIG. 6) is shown. It should beunderstood in advance that the components, layers, and functions shownin FIG. 7 are intended to be illustrative only and embodiments of theinvention are not limited thereto. As depicted, the following layers andcorresponding functions are provided:

Hardware and software layer 60 includes hardware and softwarecomponents. Examples of hardware components include: mainframes 61; RISC(Reduced Instruction Set Computer) architecture based servers 62;servers 63; blade servers 64; storage devices 65; and networks andnetworking components 66. In some embodiments, software componentsinclude network application server software 67 and database software 68.

Virtualization layer 70 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers71; virtual storage 72; virtual networks 73, including virtual privatenetworks; virtual applications and operating systems 74; and virtualclients 75. In one example, management layer 80 may provide thefunctions described below. Resource provisioning 81 provides dynamicprocurement of computing resources and other resources that are utilizedto perform tasks within the cloud computing environment. Metering andPricing 82 provide cost tracking as resources are utilized within thecloud computing environment, and billing or invoicing for consumption ofthese resources.

In one example, these resources may include application softwarelicenses. Security provides identity verification for cloud consumersand tasks, as well as protection for data and other resources. Userportal 83 provides access to the cloud computing environment forconsumers and system administrators. Service level management 84provides cloud computing resource allocation and management such thatrequired service levels are met. Service Level Agreement (SLA) planningand fulfillment 85 provide pre-arrangement for, and procurement of,cloud computing resources for which a future requirement is anticipatedin accordance with an SLA.

Workloads layer 90 provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include: mapping andnavigation 91; software development and lifecycle management 92; virtualclassroom education delivery 93; data analytics processing 94;transaction processing 95; and concept image generation 96, inaccordance with the one or more embodiments of the present invention.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of the invention. Asused herein, the singular forms “a,” “an” and “the” are intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “comprises”and/or “comprising,” when used in this specification, specify thepresence of stated features, steps, operations, elements, and/orcomponents, but do not preclude the presence or addition of anotherfeature, step, operation, element, component, and/or group thereof.

At least one embodiment of the present invention may provide abeneficial effect such as, for example, using a multimodal machinelearning algorithm to learn a generalized concept of higher-order humanbehavior, and based at least thereon, generating one or more visualconcept images as output.

The descriptions of the various embodiments of the present inventionhave been presented for purposes of illustration, but are not intendedto be exhaustive or limited to the embodiments disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope and spirit of the describedembodiments. The terminology used herein was chosen to best explain theprinciples of the embodiments, the practical application or technicalimprovement over technologies found in the marketplace, or to enableothers of ordinary skill in the art to understand the embodimentsdisclosed herein.

1. A computer-implemented method comprising: identifying one or moreevents from input data by applying a machine learning recognition modelto the input data, wherein said identifying comprises (i) detectingmultiple entities from the input data, (ii) determining one or morebehavioral relationships among the multiple entities in the input data,and (iii) generating, via the machine learning recognition model, avector representation of one or more human poses related to theidentified events; generating, using a machine learning interpretabilitymodel and the identified events, one or more images illustrating one ormore human poses related to the identified events, wherein saidgenerating the one or more images comprises converting the vectorrepresentation to one or more human pose representations over one ormore stick figure images; outputting the one or more generated images toat least one user; and updating the machine learning recognition modelbased at least in part on (i) the one or more generated images and (ii)input from the at least one user; wherein the method is carried out byat least one computing device.
 2. The computer-implemented method ofclaim 1, comprising: training the machine learning recognition modelusing data pertaining to human pose structures and image textureinformation. 3-4. (canceled)
 5. The computer-implemented method of claim1, wherein the input data comprise image data.
 6. Thecomputer-implemented method of claim 1, wherein the input data comprisetext data.
 7. The computer-implemented method of claim 1, wherein theinput data comprise multimodal data.
 8. The computer-implemented methodof claim 1, wherein the one or more images comprise one or more stickfigure images.
 9. A computer program product comprising a computerreadable storage medium having program instructions embodied therewith,the program instructions executable by a computing device to cause thecomputing device to: identify one or more events from input data byapplying a machine learning recognition model to the input data, whereinsaid identifying comprises (i) detecting multiple entities from theinput data, (ii) determining one or more behavioral relationships amongthe multiple entities in the input data, and (iii) generating, via themachine learning recognition model, a vector representation of one ormore human poses related to the identified events; generate, using amachine learning interpretability model and the identified events, oneor more images illustrating one or more human poses related to theidentified events, wherein said generating the one or more imagescomprises converting the vector representation to one or more human poserepresentations over one or more stick figure images; output the one ormore generated images to at least one user; and update the machinelearning recognition model based at least in part on (i) the one or moregenerated images and (ii) input from the at least one user. 10-11.(canceled)
 12. The computer program product of claim 9, wherein theinput data comprise one or more of image data, text data, and multimodaldata.
 13. A system comprising: a memory; and at least one processoroperably coupled to the memory and configured for: identifying one ormore events from input data by applying a machine learning recognitionmodel to the input data, wherein said identifying comprises (i)detecting multiple entities from the input data, (ii) determining one ormore behavioral relationships among the multiple entities in the inputdata, and (iii) generating, via the machine learning recognition model,a vector representation of one or more human poses related to theidentified events; generating, using a machine learning interpretabilitymodel and the identified events, one or more images illustrating one ormore human poses related to the identified events, wherein saidgenerating the one or more images comprises converting the vectorrepresentation to one or more human pose representations over one ormore stick figure images; outputting the one or more generated images toat least one user; and updating the machine learning recognition modelbased at least in part on (i) the one or more generated images and (ii)input from the at least one user.
 14. A computer-implemented methodcomprising: training a machine learning recognition model using datapertaining to human pose structures and image texture information;identifying one or more events from input data by applying the machinelearning recognition model to the input data, wherein said identifyingcomprises (i) detecting multiple entities from the input data, (ii)determining one or more behavioral relationships among the multipleentities in the input data, and (iii) generating, via the machinelearning recognition model, a vector representation of one or more humanposes related to the identified events; generating, using a machinelearning interpretability model and the identified events, one or moreimages illustrating one or more human poses related to the identifiedevents, wherein said generating the one or more images comprisesconverting the vector representation to one or more human poserepresentations over one or more stick figure images; outputting, via adialog system, the one or more generated images to at least one humanuser; automatically producing at least one machine-originated imagerepresentative of the one or more generated images based at least inpart on feedback from the at least one human user; updating the machinelearning recognition model based at least in part on the at least onemachine-originated image; wherein the method is carried out by at leastone computing device. 15-16.
 17. The computer-implemented method ofclaim 14, wherein the input data comprise image data.
 18. Thecomputer-implemented method of claim 14, wherein the input data comprisetext data.
 19. The computer-implemented method of claim 14, wherein theinput data comprise multimodal data.
 20. The computer-implemented methodof claim 14, wherein the one or more images comprise one or more stickfigure images.